This section introduces aspects that may be helpful in facilitating a better understanding of the invention. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.
Various methods have been developed for indoor localization using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indicators (RSSI) of WiFi for different locations can achieve tracking accuracies on the order of a few meters. However, RSSI fingerprinting suffers from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; and second it has been reported that in practice, certain devices record more complex (e.g., bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI.
As a first step, localization methods require laborious human involvement in the training phase to build so-called “fingerprint” maps for each Access Point (AP). In predictive mode, the RSSI from visible APs are matched to the fingerprints to estimate the location of a person or object. Typical algorithms such as nearest neighbor matching may involve solely the RSSI; other techniques can take advantage of time-stamping and of assumptions about the motion, and can resort to state-space models and dynamic system inference. However, fingerprint maps generally store only the mean value of RSSI, not the full distribution of the RSSI, and do not exploit information about the fluctuations of RSSI in the environment.
In addition, certain devices can record more complex distributions, complicating the fingerprinting process and introducing errors at estimation. Moreover, frequent retraining is necessary to maintain accuracy. Also, some APs may no longer be visible during estimation, for instance due to equipment failures or their roles in mobile ad-hoc networks. In addition, none of the previous methods considered probability kernels with distance-like metrics between distributions.
Therefore, there is a need for a simple methodology that takes into account the full distribution for computing similarities among fingerprints.